On 27 March 2021, the 15th annual Earth Hour will take place. Each year, millions of people across the globe return to the days before electricity and dedicate 60 minutes to lighting their candles as a symbol of commitment to the planet. Climate change, sustainability and environmental health are all pressing concerns that require more attention than what most of the population care to give. And as we redouble our efforts to rebuild the global economy and society after the pandemic, instilling sustainable practices are more important than ever. Let’s take a look at how artificial intelligence is helping organisations tackle these issues and reduce humanity’s carbon footprint.
The environmental movement started in the 1960s and gained momentum in the 1980s with the discovery of the hole in the ozone. Since then, leading academics have worked on environmental science and sustainability, only to be met with a myriad of setbacks due to insufficient funds or inadequate AI expertise to augment their research. In 2017, Microsoft set out to eliminate these obstacles and founded the AI for Earth programme, giving those working on solving environmental issues access to Microsoft’s cloud and AI tools. The AI programme has made it possible for numerous organisations to bridge skills, fund gaps, and successfully apply AI to environmental challenges.
Have you ever heard of the Pacific trash vortex? Located in the north Pacific, this collection of marine debris is made out of litter that ends up in the ocean, breaks down into smaller particles and clumps together. Marine debris impacts more than 600 species and introduces toxic pollutants into the food chain. With nearly nine million tons of plastic entering our oceans every year, manually removing this plastic would lead to massive amounts of new carbon emissions – talk about a double-edged sword.
Partnered with Microsoft’s AI for Earth Programme, The Ocean Cleanup is a NPO dedicated towards ridding the ocean of plastic through the development of advanced technologies. Their solution involves identifying plastic pollution in rivers and using machine learning to simulate how it moves to the ocean. It prevents litter in rivers from entering our oceans by means of an autonomous interceptor, and also involves the development of passive, cost-effective systems to clean up the existing plastic in the ocean.
Due to data gaps in agriculture, individuals in the agribusiness industry are forced to farm reactively instead of proactively. Current methods of data collection for agricultural decision making purposes are expensive, inefficient and result in immense greenhouse gas emissions.
Cloud Agronomics, a Microsoft AI for Earth partner, aims to solve these agricultural challenges by leveraging AI and remote-sensing technology to provide growers with detailed insights into their crops and soil. This verifiable system can analyse soil and crop nutrients, predict yields, and accurately predict carbon sequestration in soil at scale, enabling the agricultural community to proactively lower greenhouse gas emissions and bring about a new wave of sustainable agriculture.
According to the World Health Organisation, air pollution kills an estimated seven million people and costs the world economy over five trillion US dollars per annum, making it the world’s largest environmental health threat. Not only can air pollution cause disease, it can also exacerbate certain conditions, and has also been proven to cause cancer, as one of its main components, particulate matter, is classified as a carcinogen. The impact of air pollution on human health is a fast-growing concern as research continues to uncover links between numerous serious diseases across all age groups and air pollution.
Breeze Technologies, an AI for Earth partner, aims to address air pollution by using AI-powered sensors to collect air-quality data in real time. The company deploys small-scale air-quality sensors that can detect and measure a myriad of air pollutants and send the data to Microsoft Azure. From the environmental analytics cloud platform, AI and ML algorithms generate hyperlocal air-quality maps and match the air-quality challenges with an extensive catalog of clean air actions. The trained machine learning algorithm and AI recommends the most efficient interventions from the catalog, which if applied, raises the effectiveness of clean action plans significantly.
In the past few years, key industry players have made massive strides in solving environmental challenges and, as AI continues to evolve, one can only assume that the near future holds promise of an exceedingly significant environmental intelligence breakthrough.
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