Policy Indications: Universities Can Set Benchmarks Above AICTE  |  National Edu News: New findings on conjectures used in number theory  |  Science Innovations: DST INSPIRE faculty on alternative anti-cancer therapy with transgenic zebrafish  |  Science Innovations: Scientists develop gold microstructure substrate with tunable wettability  |  National Edu News: POWERGRID signs agreement to improve telecom connectivity in hilly areas of HP  |  Education Information: MeitY to establish a Quantum Computing Applications Lab, Powered by AWS  |  Education Information: NITI Aayog to Launch Second Edition of India Innovation Index 2020  |  Policy Indications: A Dialogue on National Education Policy 2020 at Nehru Centre, London   |  Policy Indications: ‘75% marks in class 12’ eligibility criteria under JEE (Main) 21-22 waived off  |  Policy Indications: Syllabus of JEE and NEET to remain unchanged for the year 2021  |  Leadership Instincts: Millennials Better Lead Than Manage  |  Guest Column: Metro Rail Vs Automobile-The Economics of Premium Public Transport Pricing  |  International Edu News: What will education look like in future  |  Leadership Instincts: Advanced Leadership Initiative welcomes its most diverse group of fellows  |  International Edu News: Diet may influence risk of aggressive prostate cancer  |  
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.