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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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Lenguaje: | eng |
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2022
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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. |
id | cern-2826384 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2022 |
record_format | invenio |
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 |
work_keys_str_mv | AT mckenzieryanpeter anintelligentairqualitymonitoringandpredictionsystemforsmartcities AT mckenzieryanpeter sustainablehep2ndedition AT mckenzieryanpeter intelligentairqualitymonitoringandpredictionsystemforsmartcities |