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In this study, the authors address the current climate concern of high CO₂ levels by testing solid forms of hydroxide for CO₂ reduction and designing a drone to fly it in ambient air!

This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.

Here the authors investigate the potential of the bioluminescent fungus Panellus stipticus to serve as a sustainable bioindicator for environmental lead contamination. Their findings demonstrate that higher lead concentrations cause a measurable decrease in fungal bioluminescence intensity over time suggesting that the fungus could be an effective tool for detecting lead in an environment.

The study explored converting Gracilaria seaweed waste—known for releasing toxic hydrogen sulfide when decomposed—into biochar as a sustainable solution for waste management and soil improvement.

Here the authors explored the development of biodegradable bioplastic films derived from mango peels as a sustainable solution to plastic pollution and greenhouse gas emissions from fruit waste. They optimized the film's mechanical properties and water resistance through adjusting processing conditions and incorporating plasticizers and a hydrophobic coating, ultimately demonstrating its potential as a bacteriostatic and biodegradable alternative to conventional plastic food wrap.

In this report, Bhardwaj and Sharma tested whether placing specific plants indoors can reduce levels of indoor air pollution that can lead to lung-related illnesses. Using machine learning, they show that plants improved overall indoor air quality and reduced levels of particulate matter. They suggest that plant-based interventions coupled with sensors may be a useful long-term solution to reducing and maintaining indoor air pollution.

The authors investigated the impact of light pollution on astrophotography.

Here the authors investigated air quality forecasting in India, comparing traditional time series models like SARIMA with deep learning models like LSTM. The research found that SARIMA models, which capture seasonal variations, outperform LSTM models in predicting Air Quality Index (AQI) levels across multiple Indian cities, supporting the hypothesis that simpler models can be more effective for this specific task.

The authors looked at the ability of eggshells to slow ice melting. They found that eggshells were able to increase ice melting time when crushed showing that they were an effective thermal barrier.

The authors looked at student behaviors around disposal of face masks. The goal of the study was to bring awareness to improper mask disposal and how the resulting litter contributes to overall environmental pollution.