Lecture on Environmental Impact and Biotechnology Solutions

Introduction

• Presenter: Octavio Delvecchio
• Theme: Impact of human activities on natural aquatic environments and biotechnology solutions
• Visual comparison: Pristine natural water vs. heavily contaminated water

Human Impact on Water Quality

• Humans are inefficient waste managers
• Large volume of wastewater produced:
  • 50 cubic meters/year per person
  • Industrial processes contribute significantly
• Wastewater Components:
  • Water plus contaminants like antibiotics, hormones, pesticides, viruses, and toxic compounds

Wastewater Treatment Challenges

• Wastewater treatment plants (WWTP):
  • Expensive and energy-consuming
  • Source of 3-5% of total anthropogenic greenhouse gas emissions
• Loss of valuable elements (nitrogen, phosphorus) in wastewater:
  • Nitrogen: Essential for proteins and fertilizers, requires fossil fuels to produce
  • Phosphorus: Essential for energy molecules, used in car batteries, limited natural sources

Circular Economy and Renewable Energy

• Need for new methods to remove contaminants and recover valuable elements from wastewater
• Reshaping wastewater cycle in the logic of a circular economy

Biotechnology for Wastewater Treatment

• Microorganisms in nature (microalgae and bacteria) can transform contaminants into valuable molecules
• Bioreactors are controlled environments for growing microalgae and bacteria

Microorganisms in Bioreactors

• Microalgae:
  • Accumulate nitrogen and phosphorus
  • Produce biofuels, bioplastics, biofertilizers
  • Perform photosynthesis, produce oxygen
• Bacteria:
  • Consume oxygen, provide vitamins, breakdown molecules
  • Support photosynthetic processes of microalgae

Mathematical Modeling

• Development of mathematical representations of these ecosystems
• Models help understand and direct microorganism interactions
• Collaboration with biologists and microbiologists for real data

Artificial Intelligence (AI) in Wastewater Treatment

• Numerical approach using AI to deal with biological complexity
• AI and artificial neural networks (ANN):
  • ANN can learn from data, identify unknown inhibitory or limiting factors
  • AI can enhance microorganism interactions, reduce gas emissions, and provide clean water
• Potential of AI in a broader environmental context (e.g., tracking deforestation, air and water quality)

Conclusion

• AI could play a critical role in environmental and ecological efforts
• Aim: Use AI for climate change mitigation and sustainable future

"Merci" (Applause)