Parent Interventions: Pallikkutam GlobalConnect#4 - Are You Eating the Right Food to Fight Covid-19?  |  Science Innovations: Three-Ply Masks Effective Against Covid-19: Texas Tech University  |  Science Innovations: Natural Rainbow Colours Produced  |  Technology Inceptions: Muscope, World’s Smallest Microscope  |  Science Innovations: Ultrasensitive Tactile Sensors for Robots  |  Policy Indications: How Materials Science Helps Contain Contain Covid-19 Spread  |  National Edu News: IIT Hyderabad and PharmCADD signed a pact for the co-development of new drugs   |  Teacher Insights: Be Game  |  Health Monitor: Understanding ‘Haemorrhage'  |  National Edu News: Pallikkutam GlobalConnect#3 on 'Innovative Tools for Effective Teaching'  |  Expert Counsel: The Nine Dash Line  |  National Edu News: Astronomers Find One Group of Appearing and Disappearing Stars  |  Teacher Insights: Bird Book for Children to Love Nature  |  International Edu News: New Model to Fight Social Media Deep Fakes  |  Teacher Insights: Universal Lunch Makes Students Healthier  |  
April 23, 2020 Thursday 05:02:26 PM IST

Predict Air Pollution With AI

International Edu News

A new technology to predict air pollution levels in advance has been developed by computer scientists at Loughborough University. It uses artificial intelligence (AI) to predict particulate matter of less than 2.5 microns in diameter. Presence of particulate matter (PM2.5) reduces visibility and creates haziness in cities. Being too small, the particulate matter can easily enter the lungs and then the bloodstream resulting in cardiovascular, cerebrovascular and respiratory impacts. The system is capable of predicting from one hour to several hours or even upto two days in advance, the causes for the increase in PM2.5 including weather, seasonal and environmental factors and it can also be used as a tool for air pollution analysis in a carbon credit trading system. The system’s uncertainty analysis and ability to understand factors that affect PM2.5 are particularly important as this will allow potential end-users, policymakers and scientists to better understand related causes of PM2.5 and how reliable the prediction is.