Plant diversity in the wetlands of Phobji and Gangtey represents an important aspect of the overall wetland ecosystem. However, over the years, the increasing trend in population has initiated the building of many infrastructures and accommodations which lie at close proximity to the core wetland area. The plant diversity in human settled area and core undisturbed area was studied, with an objective to assess the influence of human settlement on the plant diversity. A belt transect method was used for the purpose of vegetation survey and Shannon Wiener diversity was calculated using the relative dominance. Plants were analyzed for their diversity, richness, dominance and invasive nature. Additionally, soil and water parameters were also tested to see the current situation of the pH and nutrient levels. A total of 136 species belonging to 39 families was identified in both the areas. The overall diversity index and species richness was found to be higher in the undisturbed areas. Soil analysis did not show much of a difference in physical and chemical parameters except for a slight difference in potassium content. Similarly, water parameters did not show much difference but only showed variation in calcium content. The findings of the study indicated towards the increasing influence of human settlement in the wetland.
Background
Microbial polyhydroxyalkanoates (PHAs) produced using renewable resources could be the best alternative for conventional plastics. Despite their incredible potential, commercial production of PHAs remains very low. Nevertheless, sincere attempts have been made by researchers to improve the yield and economic viability of PHA production by utilizing low‐cost agricultural or industrial wastes. In this context, the use of efficient microbial culture or consortia, adoption of experimental design to trace ideal growth conditions, nutritional requirements, and intervention of metabolic engineering tools have gained significant attention.
Purpose and scope
This review has been structured to highlight the important microbial sources for PHA production, use of conventional and non‐conventional substrates, product optimization using experimental design, metabolic engineering strategies, and global players in the commercialization of PHA in the past two decades. The challenges about PHA recovery and analysis have also been discussed which possess indirect hurdle while expanding the horizon of PHA‐based bioplastics.
Summary
Selection of appropriate microorganism and substrate plays a vital role in improving the productivity and characteristics of PHAs. Experimental design‐based bioprocess, use of metabolic engineering tools, and optimal product recovery techniques are invaluable in this dimension.
Conclusion
Optimization strategies, which are being explored in isolation, need to be logically integrated for the successful commercialization of microbial PHAs.
The rate and extent of microbial polyhydroxyalkanoates (PHAs) production rely on the availability of substrates, growth of microbial biomass, and intracellular accumulation of polymer under nitrogen‐limited conditions. The dynamics of PHAs production captured through various structured or unstructured models can be extended to design an optimal feeding strategy for process intensification. Large variability in process assumptions, choices of kinetics, and model complexity is expected depending on substrate(s), microbial metabolism, and discretization of the process under consideration. This communication attempts to review the estimation of stoichiometric yield coefficients, metabolic modelling, and choices of unstructured kinetics in microbial PHA production. Implementational irregularities in parameter estimation and quality check in modelling exercises have also been reviewed. It is observed that the scope of the majority of the “modelling” studies is confined to the estimation of stoichiometric parameters with limited utility. In dynamic models, microbial growth is often described using either Monod or logistic variants, while PHAs production adopts a Luedeking–Piret expression with or without substrate inhibition. Though model selection, regression with experimental data, parameter estimation, and model validation are integral parts of the exercise, very few provide sufficient coverage on all those aspects. Application of the model to control or optimize the bioprocess has rarely been attempted.
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