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An Intelligent Air Quality Monitoring and Prediction System for Smart Cities

<!--HTML-->In 2019 99% of people were found to breathe air that exceeds WHO air quality limits, and 7 million people die from air pollution annually. This motivated the formation of the South African Consortium of Air Quality Monitoring. The international consortium was founded with the goal...

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Autor principal: Mckenzie, Ryan Peter
Lenguaje:eng
Publicado: 2022
Materias:
Acceso en línea:http://cds.cern.ch/record/2826384
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author Mckenzie, Ryan Peter
author_facet Mckenzie, Ryan Peter
author_sort Mckenzie, Ryan Peter
collection CERN
description <!--HTML-->In 2019 99% of people were found to breathe air that exceeds WHO air quality limits, and 7 million people die from air pollution annually. This motivated the formation of the South African Consortium of Air Quality Monitoring. The international consortium was founded with the goal of bringing together government institutions, HEP research institutions, and private enterprises into a mutually beneficial ecosystem to deliver an industry-disrupting open-source low-cost intelligent Internet-of-Things (IoT) air quality monitoring and prediction system for the benefit of the world. The system combines existing air quality sensors with a low-cost IoT network architecture to enable the use of Artificial Intelligence (AI) for air quality predictions. The expertise has been developed through the maintenance, operations, and Phase-II upgrade of the electronics of the ATLAS Hadronic Calorimeter as well as the use of machine learning techniques during data analysis of ATLAS data.
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institution Organización Europea para la Investigación Nuclear
language eng
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spelling cern-28263842022-11-02T22:03:34Zhttp://cds.cern.ch/record/2826384engMckenzie, Ryan PeterAn Intelligent Air Quality Monitoring and Prediction System for Smart CitiesSustainable HEP - 2nd editionTH institutes<!--HTML-->In 2019 99% of people were found to breathe air that exceeds WHO air quality limits, and 7 million people die from air pollution annually. This motivated the formation of the South African Consortium of Air Quality Monitoring. The international consortium was founded with the goal of bringing together government institutions, HEP research institutions, and private enterprises into a mutually beneficial ecosystem to deliver an industry-disrupting open-source low-cost intelligent Internet-of-Things (IoT) air quality monitoring and prediction system for the benefit of the world. The system combines existing air quality sensors with a low-cost IoT network architecture to enable the use of Artificial Intelligence (AI) for air quality predictions. The expertise has been developed through the maintenance, operations, and Phase-II upgrade of the electronics of the ATLAS Hadronic Calorimeter as well as the use of machine learning techniques during data analysis of ATLAS data.oai:cds.cern.ch:28263842022
spellingShingle TH institutes
Mckenzie, Ryan Peter
An Intelligent Air Quality Monitoring and Prediction System for Smart Cities
title An Intelligent Air Quality Monitoring and Prediction System for Smart Cities
title_full An Intelligent Air Quality Monitoring and Prediction System for Smart Cities
title_fullStr An Intelligent Air Quality Monitoring and Prediction System for Smart Cities
title_full_unstemmed An Intelligent Air Quality Monitoring and Prediction System for Smart Cities
title_short An Intelligent Air Quality Monitoring and Prediction System for Smart Cities
title_sort intelligent air quality monitoring and prediction system for smart cities
topic TH institutes
url http://cds.cern.ch/record/2826384
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AT mckenzieryanpeter sustainablehep2ndedition
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