Department of Civil Engineering, SCMS School of Engineering & Technology, Kochi, Kerala, India.
Department of Computer Science & Engineering, SCMS School of Engineering & Technology, Kochi, Kerala, India.
Environ Sci Pollut Res Int. 2023 Jan;30(4):10414-10425. doi: 10.1007/s11356-022-22836-w. Epub 2022 Sep 8.
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 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.
社交媒体平台是公众用来传播意识或引起对某个问题或关注点关注的新兴方法之一。本研究展示了如何使用公众在推特上的回复来对城市地区的空气污染进行定性监测。使用基于自注意力网络的机器学习技术，在 2019-2020 年期间提取了印度德里有关空气质量的推文。对这些推文进行了清理、分类，并分为 3 类质量，即空气质量差、空气质量好和噪声或中性推文。本研究使用了一个多层分类模型，第一层是嵌入层，第二层是双向长短时记忆（BiLSTM）层。然后，使用“spaCy”对分类推文和德里在研究期间的连续环境空气质量监测站（CAAQMS）中提取的数据进行相似性分析，设计了一种从推文中估计 PM 浓度的方法。这种估计的准确性在极端空气质量条件（极好或严重）下很高（80-99%），而在空气质量适度变化时较低。该方法的应用取决于空气质量的可感知变化、推特参与度和公众的环境意识。
