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Assessment of urban air quality from Twitter communication using self-attention network and a multilayer classification model

Social media platforms are one of the prominent new-age methods used by public for spreading awareness or drawing attention on an issue or concern. This study demonstrates how the twitter responses of public can be used for qualitative monitoring of air pollution in an urban area. Tweets discussing...

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Autores principales: Kumbalaparambi, Thushara Sudheish, Menon, Ratish, Radhakrishnan, Vishnu P, Nair, Vinod P
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9453714/
https://www.ncbi.nlm.nih.gov/pubmed/36074292
http://dx.doi.org/10.1007/s11356-022-22836-w
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author Kumbalaparambi, Thushara Sudheish
Menon, Ratish
Radhakrishnan, Vishnu P
Nair, Vinod P
author_facet Kumbalaparambi, Thushara Sudheish
Menon, Ratish
Radhakrishnan, Vishnu P
Nair, Vinod P
author_sort Kumbalaparambi, Thushara Sudheish
collection PubMed
description Social media platforms are one of the prominent new-age methods used by public for spreading awareness or drawing attention on an issue or concern. This study demonstrates how the twitter responses of public can be used for qualitative monitoring of air pollution in an urban area. Tweets discussing about air quality in Delhi, India, were extracted during 2019–2020 using a machine learning technique based on self-attention network. These tweets were cleaned, sorted, and classified into 3-class quality viz. poor air quality, good air quality, and noise or neutral tweets. The present study used a multilayer classification model with first layer as an embedding layer and second layer as bi-directional long-short term memory (BiLSTM) layer. A method was then devised for estimating PM(2.5) concentration from the tweets using ‘spaCy’ similarity analysis of classified tweets and data extracted from Continuous Ambient Air Quality Monitoring Stations (CAAQMS) in Delhi for the study period. The accuracy of this estimation was found to be high (80–99%) for extreme air quality conditions (extremely good or severe) and lower during moderate variations in air quality. Application of this methodology depended on perceivable changes in air quality, twitter engagement, and environmental consciousness among public. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-022-22836-w.
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spelling pubmed-94537142022-09-08 Assessment of urban air quality from Twitter communication using self-attention network and a multilayer classification model Kumbalaparambi, Thushara Sudheish Menon, Ratish Radhakrishnan, Vishnu P Nair, Vinod P Environ Sci Pollut Res Int Research Article Social media platforms are one of the prominent new-age methods used by public for spreading awareness or drawing attention on an issue or concern. This study demonstrates how the twitter responses of public can be used for qualitative monitoring of air pollution in an urban area. Tweets discussing about air quality in Delhi, India, were extracted during 2019–2020 using a machine learning technique based on self-attention network. These tweets were cleaned, sorted, and classified into 3-class quality viz. poor air quality, good air quality, and noise or neutral tweets. The present study used a multilayer classification model with first layer as an embedding layer and second layer as bi-directional long-short term memory (BiLSTM) layer. A method was then devised for estimating PM(2.5) concentration from the tweets using ‘spaCy’ similarity analysis of classified tweets and data extracted from Continuous Ambient Air Quality Monitoring Stations (CAAQMS) in Delhi for the study period. The accuracy of this estimation was found to be high (80–99%) for extreme air quality conditions (extremely good or severe) and lower during moderate variations in air quality. Application of this methodology depended on perceivable changes in air quality, twitter engagement, and environmental consciousness among public. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-022-22836-w. Springer Berlin Heidelberg 2022-09-08 2023 /pmc/articles/PMC9453714/ /pubmed/36074292 http://dx.doi.org/10.1007/s11356-022-22836-w Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Research Article
Kumbalaparambi, Thushara Sudheish
Menon, Ratish
Radhakrishnan, Vishnu P
Nair, Vinod P
Assessment of urban air quality from Twitter communication using self-attention network and a multilayer classification model
title Assessment of urban air quality from Twitter communication using self-attention network and a multilayer classification model
title_full Assessment of urban air quality from Twitter communication using self-attention network and a multilayer classification model
title_fullStr Assessment of urban air quality from Twitter communication using self-attention network and a multilayer classification model
title_full_unstemmed Assessment of urban air quality from Twitter communication using self-attention network and a multilayer classification model
title_short Assessment of urban air quality from Twitter communication using self-attention network and a multilayer classification model
title_sort assessment of urban air quality from twitter communication using self-attention network and a multilayer classification model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9453714/
https://www.ncbi.nlm.nih.gov/pubmed/36074292
http://dx.doi.org/10.1007/s11356-022-22836-w
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