IJEP 41(11): 1284-1289 : Vol. 41 Issue. 11 (November 2021)
Geeta Singh*, Anirudh Goel, Shaurya Gulati, Mughil M. and Gaurav Karhana
Delhi Technological University, Department of Environmental Engineering, New Delhi, India
Abstract
In recent years the capital city of India, Delhi, has experienced unprecedented levels of air pollution during October and November. Growing digitization efforts combined with low data prices facilitated an increase in digital media usage in India. The main objective of this study is to examine the relationship between air pollution levels and digital media activity and determine popular perception regarding digital media’s influence in combating air pollution. The aforementioned relationship was established by evaluating the average number of searches on google from August 2019 to January 2020 in conjunction with the spikes in pollutant concentration. Further, a qualitative analysis was conducted using an assiduously drafted questionnaire which was analyzed by the CART decision tree to explore the attitude and behaviour of people and their opinion on the influence of digital media to combat air pollution in Delhi. The findings underscored that digital media activity related to air pollution was quite high for October and November, a period which saw an acute increase in air pollution levels. The survey highlighted google as the most preferred and influential source of information related to air pollution. The socio-demographic characteristics played an essential role in the respondent’s preferences. The results of the survey underlined that digital media platforms are influential in fostering pro-environmental behaviours among the citizens. However, citizens are reluctant to take action against air pollution as they feel their role is limited.
Keywords
Air pollution, Digital media, Pro-environmental behavior, Socio-demographic
References
- Times, T. 2020. Dirty air : How India became the most polluted country on earth. Available at : https://ig.ft.com/india-pollution/.
- BBC News. 2020. why is India’s pollution much worse than China’s. Available at : https://www. bbc.com/news/world-asia-50298972.
- Cpebenvis.nic.in. 2020. Welcome to ENVIS. Central Pollution Control Board, Delhi, India. Available at : http://cpcbenvis.nic.in/air pollution/finding. htm.
- Statista. 2020. Number of internet users in selected countries. 2019 Statista. Available at : https://www. statista.com/statistics/2622966/number of-internet-users-in-selected-countries/.
- Statista. 2020. Internet users in India. Available at : https://www.statista.com/statistics/255146/number of-internet-users-in-india/.
- Sannam S4. 2020. Digital trends 2019 and social media landscape in India. Available at : https://sannams4.com/digital-and-social-media-landscape-in-India.
- Gupta, A.R. 2019. Air pollution in Delhi – Policy, people and perception. Observer Research Foundation (ORF), New Delhi.
- NDTV-Dettol Banega Swasth Swachh India. 2020. People know about air pollution but lack awareness about cause and effect: study. Available at : http://swachhindia.ndtv.com/air-pollution-perception-study-on-air-quality-finds-gaps-in-awareness-on-causes-solutions-274841/.
- Jiang, W., et al. 2015. Using social media to detect outdoor air pollution and monitor air quality index (AQI) : A geo-targated spatiotemporal analysis framework with Sina Weibo (Chinese twitter). Plos One. 10(10):e0141185.
- Shannon, C. 2001. A mathematical theory of communication. ACM SIG MOBILE. Mobile Computing Commun. Review. 5(1):3.
- Gurajala, S., S. Dhaniyala and J. Matthews. 2019. Understanding public response to air quality using tweet analysis. Social Media Society. 5(3). DOI: 10.1177/2056305119867656.
- Blei, D.M., A.Y. Ng and M.I. Joran. 2003. Latent dirichlet allocation. J. Machine Learning Res., 3:993-1022.
- Carducci, A., et al. 2016. Air pollution and health : Study of citizen’s attitudes and behaviours using multiple sources. European J. Public Health. DOI: 10.1093/eurpub/ckw174.022.
- Chiu, C.C.J. and S.D. Young. 2015. The relationship between online social network use, sexual risk behaviours and HIV sero-status among a sample of predominately African American and Latino men who have sex with men (MSM) social media users. AIDS Behaviour. 19:98-105.
- Faraon, M., G. Stenberg and M. Kaipainen. 2014. Political compaigning 2.0: The influence of online news and social networking sites on attitudes and behaviour. JeDEM-e.J.eDemocracy Open Govt., 6(3):231-247.
- Levy, A., N. Orion and Y. Leshem. 2016. Variables that influence the environmental behaviour of adults. Env. Education Res., 24(3):307-325.
- Medium. 2020. Dicision-Trees-A simple way to visualize a decision. Available at : https://medium. com/greyatom/decision-trees-a-simple-way-to-visualize-a-decision-de506a403aeb.
- Sawitri, D., H. Hadiyanto and S. Hadi. 2015. Pro-environmental behaviour from a social congnitive theory perspective. Procedia Env. Sci., 23:27-33.
