Computational tools for understanding air pollution

Adhikary, Rishiraj; Batra, Nipun

doi:10.1145/3410530.3414327

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…The second research area focuses on understanding peoples' experiences of air quality. For instance, leveraging social media websites such as Twitter to collect air quality information [2,22,93]. The use of chatbots integrated with IoT to provide easy access to air quality information along with features such as alarm services and geo-queries [66].…”

Section: Air Quality and Technology Designmentioning

confidence: 99%

The Human-Air Interface: Responding To Poor Air Quality Through Lived Experience and Digital Information

Gupta

Eden

2022

Designing Interactive Systems Conference

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The Air Quality Index (AQI) is a set of numerical, color-coded metrics used to communicate air pollution levels. AQI data is available through a variety of mediums such as mobile apps, websites and public displays. However, the information communicated through these may not be easily interpretable to everyone. Especially when scientifc data and associated representations are used to convey information to communities whose shared knowledge and practices are signifcantly diferent from Western scientifc contexts. We discuss fndings from a qualitative study conducted in Delhi, India to understand how residents across both low-and high-resourced communities, assess their personal risk around air pollution and the safety measures they take to mitigate that risk. We reveal incompatibilities between the air-quality information displayed in digital platforms and whether that information is interpretable for people with diferent cultural sensitivities. We conclude with design implications for improving the interpretability and relevance of air quality interfaces. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Empirical studies in HCI; Field studies.

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Section: Air Quality and Technology Designmentioning

confidence: 99%

The Human-Air Interface: Responding To Poor Air Quality Through Lived Experience and Digital Information

Gupta

Eden

2022

Designing Interactive Systems Conference

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Comparative analysis of Air Quality Index prediction using deep learning algorithms

Mishra

Gupta

2023

Spat. Inf. Res.

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Vartalaap

Adhikary

Patel

Srivastava

et al. 2021

Proc. ACM Hum.-Comput. Interact.

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Air pollution is a global challenge for cities across the globe. Understanding the public perception of air pollution can help policymakers engage better with the public and appropriately introduce policies. Accurate public perception can also help people to identify the health risks of air pollution and act accordingly. Unfortunately, current techniques for determining perception are not scalable: it involves surveying few hundred people with questionnaire-based surveys. Using the advances in natural language processing (NLP), we propose a more scalable solution called Vartalaap to gauge public perception of air pollution via the microblogging social network Twitter. We curated a dataset of more than 1.2M tweets discussing Delhi-specific air pollution. We find that (unfortunately) the public is supportive of unproven mitigation strategies to reduce pollution, thus risking their health due to a false sense of security. We also find that air quality is a year-long problem, but the discussions are not proportional to the level of pollution and spike up when pollution is more visible. The information required by Vartalaap is publicly available and, as such, it can be immediately applied to study different societal issues across the world.

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Computational tools for understanding air pollution

Cited by 3 publications

References 15 publications

The Human-Air Interface: Responding To Poor Air Quality Through Lived Experience and Digital Information

The Human-Air Interface: Responding To Poor Air Quality Through Lived Experience and Digital Information

Comparative analysis of Air Quality Index prediction using deep learning algorithms

Vartalaap

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