This research compares Earth’s energy balance to a personal budget and examines how aerosols—especially black carbon—disturb that balance. By simulating how black carbon interacts with cloud droplets and light, the study helps improve understanding of climate impacts. The goal is better climate modeling and reducing harmful atmospheric pollution.

This research examines how atmospheric aerosols influence cloud formation and rainfall, particularly under turbulent conditions. Using a laboratory cloud chamber and computer modeling, the study investigates how particle size and concentration affect droplet growth. The findings aim to improve climate models and weather forecasting in both polluted and clean environments.

My research uses artificial intelligence to detect water pollution by analysing DNA traces left by aquatic species. Instead of relying on visual signs or costly expert identification, supervised machine learning reads species patterns to determine water quality. The method is faster, cheaper, and more accurate than traditional analysis.