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.
One of the prominent new-age methods used by today’s population in spreading awareness or drawing attention on an issue or concern is through social media platforms. In this study, the responses of general public to air quality that they share on a popular social media platform – Twitter, was taken as a virtual quantity that helps in measuring and analyzing the prevalent air quality. Machine learning technique based on self attention network was used to sort, clean and classify large amount of air pollution related Twitter responses extracted during 2019-2020 at Delhi in India. The temporal correlation of tweet response volumes with the ground monitored concentrations of air pollution and word cloud analysis were used to analyze the attitude of the public towards urban air quality. These analyses lead to the development of an air quality prediction model using ‘spaCy’ – similarity analysis method which attempts to predict the ground concentration range of pollutant PM2.5 from tweets received on a particular day.
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