Maejo International Journal of Energy and Environmental Communication

Environmental implications and nutrient management: Influence of essential mineral elements on sustainable growth and quality of Lentinus edodes in the Qinghai-Tibet plateau

Jing Li — 2024-06-15

The study aimed to explore how varying concentrations of calcium (Ca²⁺), magnesium (Mg²⁺), and potassium (K⁺) in water influence the growth, yield, and nutritional quality of Lentinus edodes (shiitake mushrooms), with the test strain Liao Fu No.4 (0912). L. edodes is known for its nutritional and medicinal benefits, making it an important agricultural crop, particularly on the Qinghai-Tibet Plateau, where environmental conditions are unique. The study designed five concentration gradients of these minerals based on their natural occurrence in water from different farms. Results measured the impact of these concentrations on the mycelium growth rate, fruiting body yield, agronomic traits, and the content of polysaccharides and protein in the mushrooms. The results showed that calcium, magnesium, and potassium concentrations significantly influenced both the growth and the nutritional composition of L. edodes. It was found that water containing calcium levels between 41-105 mg/L, magnesium between 7.04- 8.56 mg/L, and potassium between 7.26-8.85 mg/L promoted faster mycelium growth and higher yields. These optimal concentrations also improved the nutritional quality of the mushrooms by increasing polysaccharide and protein content in the fruiting bodies. Furthermore, the agronomic traits, including size and firmness, were enhanced under these conditions, making the mushrooms more commercially viable. In conclusion, the study provides practical insights for farmers on the Qinghai-Tibet Plateau and similar regions, suggesting that selecting water sources with these specific mineral concentrations can improve both the yield and the nutritional value of L. edodes, thereby optimizing mushroom production.

Early-warning modeling of water blooms using machine learning algorithm

Tianxiao Liu — 2024-06-29

Many environmental studies have implemented machine learning methods, including water bloom prediction. However, the need for robustness and generalization of models across different environmental conditions and geographical locations is urgent. This study implements. The Random Forest algorithm to develops a predictive model for water bloom occurrences. Using a classification model, it is a novel approach for predicting water bloom occurrences across various freshwater bodies, from reservoirs to lakes. The model demonstrates an overall accuracy of 0.74 for predicting the water blooms from multiple freshwater sources, including Inba Swamp, Takizawa Dam, Shourenji Dam, and Kasumigaura, using surveillance data from Japan Water Agency websites. The promising accuracy and intentionally introduced one-month lag of water bloom occurrence in the constructed database shows its effectiveness in anticipating water blooms. The Out-Of- Bag error rate suggests the optimal number of trees to grow in the model is 500 for space and time efficiency. Cross-validation across multiple sampling sites reveals nuanced prediction accuracies, emphasizing the importance of considering spatial variability. Predictor importance analysis identifies chlorophyll a, total nitrogen, total phosphorus, and temperature as crucial factors, with the importance of 0.18, 0.04, 0.02, and 0.02, respectively. It does not only contribute to the accuracy of the prediction model but also provides insights into the underlying dynamics of water bloom events, enhancing the understanding of water bloom dynamics and informing proactive environmental management strategies.

Alkaline hydrolysis as a simple method for converting chicken manure fertilizer into feedstock for ectoine production by Halomonas elongata cell factory

Huynh Cong Khanh — 2024-06-04

Changes in dietary habits in modern-day Japan have led to a significant expansion in the country’s livestock industry. One notable change is the increased consumption of eggs due to their high-quality protein content and affordability. This significantly expanded the poultry industry to the extent that it raised environmental concerns, especially regarding the increased waste production. Major solid waste from the poultry industry is chicken manure (CM), commonly treated through composting into fertilizer. However, there is currently an oversupply of CM fertilizer globally due to a large amount of CM being produced. We address this issue by developing a simple alkaline hydrolysis method to convert the nitrogen-rich CM fertilizer into media for culturing bacterial cell factories. Our result shows that the high-salinity CM-derived media developed here can sustain the growth of Halomonas elongata OUT30018. This moderately halophilic bacterium biosynthesizes and accumulates valuable chemicals such as ectoine (ECT) in the cell. We observed that H. elongata OUT30018 cultured in a CM-derived medium containing 15% w/v NaCl could accumulate 335 μmol of ECT per g cell fresh weight (CFW), a concentration comparable to that obtained in the cells cultured in common synthetic media. Furthermore, this concentration was increased to 393 μmol/g CFW when the CM-derived medium was supplemented with 4% w/v xylose. The work reported here represents the initial stage of developing new technology for managing CM. Implementation of this technology would improve the sustainability of both the poultry and the fermentation industries.

Dye-sensitized solar cells with nano TiO₂ quantum dots and foliar pigments: A review

Maria Onyemowo Oteikwu — 2024-05-25

The integration of plant pigments, quantum dots (QDs), and carbon nanotubes (CNTs) into dye-sensitized solar cells (DSSCs) is a notable progress in the effort to achieve carbon neutrality. Dye-sensitized solar cells (DSSCs) provide a sustainable alternative to conventional solar cells that depend on artificial dyes by utilizing renewable and environmentally friendly natural dyes produced from plant pigments. Quantum dots are employed to augment the effectiveness of light absorption in DSSCs, while carbon nanotubes promote electron transport and minimize charge recombination, resulting in a higher total efficiency. This integration not only decreases the environmental impact linked to the manufacturing of solar cells but also fosters a circular economy by utilizing agricultural by-products as sources of natural dyes. In addition, DSSCs have a reduced energy requirement for manufacturing compared to silicon-based solar cells, making them well-suited for widespread implementation in underdeveloped areas with insufficient infrastructure. Continued research and development efforts are focused on improving the performance and scalability of DSSCs. These technologies have the potential to be crucial in meeting global carbon neutrality targets and reducing the effects of climate change.